AI Readiness

AI Readiness describes your current state: how well your data, processes, people and systems can support AI today. An AI strategy describes intent: which outcomes you want from AI and which use cases get priority and budget. The first is a diagnosis, the second is a choice. Set a strategy without knowing where you stand and you get plans the organization cannot execute. Run the assessment without a strategy and you get scattered pilots that never add up. Establish both within the same planning cycle.

AI Readiness should not sit with the IT department alone. Strategy, vision and culture depend on decisions only the leadership team can make, while processes and data belong to the people who do the work every day. What tends to work is a single owner at leadership level who protects the priority, supported by a named person per dimension who supplies the facts and carries out the agreed improvement. Without that split, the assessment produces a report and nothing follows it.

A manual export is a workable starting point. For a first, narrowly scoped application an export is often enough, provided the records are complete enough for the question you are asking, captured consistently, and owned by someone who can correct errors. An integration only becomes a requirement once you repeat the exercise, because from that point manual handling starts to drive both turnaround time and reliability. Start with the export, and plan the integration for the moment the application proves it will stay in use.

The score points the direction and the proof sits in what changes in the work. Use the assessment as a reference point: it shows which dimension is moving and which is lagging. Alongside it, track the chosen process directly, whether turnaround time falls, whether fewer outputs need manual correction, and whether the team still uses the system once the launch attention fades. Record those measures before you start so you have something to compare against. The clearest signal is the next application reaching production noticeably faster.

AI Readiness models differ because there is no industry standard. Every model, ours included, divides the same ground its own way: one groups data and infrastructure together, another separates them. Underneath the labels you keep meeting the same areas, namely data, technology, processes, people and strategy. So pick one model and stay with it, because a score only means something when you can compare it with an earlier reading from the same model. Switching models leaves you with results that cannot be compared.